
"""simple docstring"""






import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation




def __magic_name__	(				_lowerCamelCase:		List[str]			)					->						Optional[Any]:

	'''simple docstring'''




	lowerCAmelCase												=  384
	lowerCAmelCase												=  7
	if "tiny" in model_name:
		lowerCAmelCase												=  96
		lowerCAmelCase												=  (2, 2, 6, 2)
		lowerCAmelCase												=  (3, 6, 12, 24)
	elif "small" in model_name:
		lowerCAmelCase												=  96
		lowerCAmelCase												=  (2, 2, 18, 2)
		lowerCAmelCase												=  (3, 6, 12, 24)
	elif "base" in model_name:
		lowerCAmelCase												=  128
		lowerCAmelCase												=  (2, 2, 18, 2)
		lowerCAmelCase												=  (4, 8, 16, 32)
		lowerCAmelCase												=  12
		lowerCAmelCase												=  512
	elif "large" in model_name:
		lowerCAmelCase												=  192
		lowerCAmelCase												=  (2, 2, 18, 2)
		lowerCAmelCase												=  (6, 12, 24, 48)
		lowerCAmelCase												=  12
		lowerCAmelCase												=  768

	# set label information
	lowerCAmelCase												=  150
	lowerCAmelCase												=  '''huggingface/label-files'''
	lowerCAmelCase												=  '''ade20k-id2label.json'''
	lowerCAmelCase												=  json.load(open(hf_hub_download(_lowerCamelCase,					_lowerCamelCase,					repo_type='''dataset'''			),					'''r'''			)			)
	lowerCAmelCase												=  {int(_lowerCamelCase			): v for k, v in idalabel.items()}
	lowerCAmelCase												=  {v: k for k, v in idalabel.items()}

	lowerCAmelCase												=  SwinConfig(
	    embed_dim=_lowerCamelCase,					depths=_lowerCamelCase,					num_heads=_lowerCamelCase,					window_size=_lowerCamelCase,					out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''],					)
	lowerCAmelCase												=  UperNetConfig(
	    backbone_config=_lowerCamelCase,					auxiliary_in_channels=_lowerCamelCase,					num_labels=_lowerCamelCase,					idalabel=_lowerCamelCase,					labelaid=_lowerCamelCase,					)

	return config




def __magic_name__	(				_lowerCamelCase:		List[str]			)					->						List[str]:

	'''simple docstring'''




	lowerCAmelCase												=  []

	# fmt: off
	# stem
	rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')			)
	rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')			)
	rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')			)
	rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')			)
	# stages
	for i in range(len(config.backbone_config.depths			)			):
		for j in range(config.backbone_config.depths[i]			):
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")			)
			rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")			)

		if i < 3:
			rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")			)
			rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")			)
			rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.bias""")			)
		rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")			)
		rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")			)

	# decode head
	rename_keys.extend(
	    [
	        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
	        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
	        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
	        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
	    ]			)
	# fmt: on

	return rename_keys




def __magic_name__	(				_lowerCamelCase:		List[Any],					_lowerCamelCase:		Tuple,					_lowerCamelCase:		List[Any]			)					->						str:

	'''simple docstring'''




	lowerCAmelCase												=  dct.pop(_lowerCamelCase			)
	lowerCAmelCase												=  val




def __magic_name__	(				_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		Optional[int]			)					->						str:

	'''simple docstring'''




	lowerCAmelCase												=  [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
	for i in range(len(backbone_config.depths			)			):
		lowerCAmelCase												=  num_features[i]
		for j in range(backbone_config.depths[i]			):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			lowerCAmelCase												=  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""			)
			lowerCAmelCase												=  state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""			)
			# next, add query, keys and values (in that order) to the state dict
			lowerCAmelCase												=  in_proj_weight[:dim, :]
			lowerCAmelCase												=  in_proj_bias[: dim]
			lowerCAmelCase												=  in_proj_weight[
			    dim : dim * 2, :
			]
			lowerCAmelCase												=  in_proj_bias[
			    dim : dim * 2
			]
			lowerCAmelCase												=  in_proj_weight[
			    -dim :, :
			]
			lowerCAmelCase												=  in_proj_bias[-dim :]
			# fmt: on




def __magic_name__	(				_lowerCamelCase:		List[str]			)					->						int:

	'''simple docstring'''




	lowerCAmelCase							,       lowerCAmelCase												=  x.shape
	lowerCAmelCase												=  x.reshape(_lowerCamelCase,					4,					in_channel // 4			)
	lowerCAmelCase												=  x[:, [0, 2, 1, 3], :].transpose(1,					2			).reshape(_lowerCamelCase,					_lowerCamelCase			)
	return x




def __magic_name__	(				_lowerCamelCase:		List[Any]			)					->						Any:

	'''simple docstring'''




	lowerCAmelCase							,       lowerCAmelCase												=  x.shape
	lowerCAmelCase												=  x.reshape(_lowerCamelCase,					in_channel // 4,					4			)
	lowerCAmelCase												=  x[:, :, [0, 2, 1, 3]].transpose(1,					2			).reshape(_lowerCamelCase,					_lowerCamelCase			)

	return x




def __magic_name__	(				_lowerCamelCase:		Dict			)					->						List[str]:

	'''simple docstring'''




	lowerCAmelCase												=  x.shape[0]
	lowerCAmelCase												=  x.reshape(4,					in_channel // 4			)
	lowerCAmelCase												=  x[[0, 2, 1, 3], :].transpose(0,					1			).reshape(_lowerCamelCase			)
	return x




def __magic_name__	(				_lowerCamelCase:		Union[str, Any]			)					->						int:

	'''simple docstring'''




	lowerCAmelCase												=  x.shape[0]
	lowerCAmelCase												=  x.reshape(in_channel // 4,					4			)
	lowerCAmelCase												=  x[:, [0, 2, 1, 3]].transpose(0,					1			).reshape(_lowerCamelCase			)
	return x




def __magic_name__	(				_lowerCamelCase:		Optional[Any],					_lowerCamelCase:		List[Any],					_lowerCamelCase:		str			)					->						str:

	'''simple docstring'''




	lowerCAmelCase												=  {
	    '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
	    '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
	    '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
	    '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
	}
	lowerCAmelCase												=  model_name_to_url[model_name]
	lowerCAmelCase												=  torch.hub.load_state_dict_from_url(_lowerCamelCase,					map_location='''cpu''',					file_name=_lowerCamelCase			)[
	    '''state_dict'''
	]

	for name, param in state_dict.items():
		print(_lowerCamelCase,					param.shape			)

	lowerCAmelCase												=  get_upernet_config(_lowerCamelCase			)
	lowerCAmelCase												=  UperNetForSemanticSegmentation(_lowerCamelCase			)
	model.eval()

	# replace "bn" => "batch_norm"
	for key in state_dict.copy().keys():
		lowerCAmelCase												=  state_dict.pop(_lowerCamelCase			)
		if "bn" in key:
			lowerCAmelCase												=  key.replace('''bn''',					'''batch_norm'''			)
		lowerCAmelCase												=  val

	# rename keys
	lowerCAmelCase												=  create_rename_keys(_lowerCamelCase			)
	for src, dest in rename_keys:
		rename_key(_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase			)
	read_in_q_k_v(_lowerCamelCase,					config.backbone_config			)

	# fix downsample parameters
	for key, value in state_dict.items():
		if "downsample" in key:
			if "reduction" in key:
				lowerCAmelCase												=  reverse_correct_unfold_reduction_order(_lowerCamelCase			)
			if "norm" in key:
				lowerCAmelCase												=  reverse_correct_unfold_norm_order(_lowerCamelCase			)

	model.load_state_dict(_lowerCamelCase			)

	# verify on image
	lowerCAmelCase												=  '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
	lowerCAmelCase												=  Image.open(requests.get(_lowerCamelCase,					stream=_lowerCamelCase			).raw			).convert('''RGB'''			)

	lowerCAmelCase												=  SegformerImageProcessor()
	lowerCAmelCase												=  processor(_lowerCamelCase,					return_tensors='''pt'''			).pixel_values

	with torch.no_grad():
		lowerCAmelCase												=  model(_lowerCamelCase			)
		lowerCAmelCase												=  outputs.logits

	print(logits.shape			)
	print('''First values of logits:''',					logits[0, 0, :3, :3]			)
	# assert values
	if model_name == "upernet-swin-tiny":
		lowerCAmelCase												=  torch.tensor(
		    [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]			)
	elif model_name == "upernet-swin-small":
		lowerCAmelCase												=  torch.tensor(
		    [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]			)
	elif model_name == "upernet-swin-base":
		lowerCAmelCase												=  torch.tensor(
		    [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]			)
	elif model_name == "upernet-swin-large":
		lowerCAmelCase												=  torch.tensor(
		    [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]			)
	print('''Logits:''',					outputs.logits[0, 0, :3, :3]			)
	assert torch.allclose(outputs.logits[0, 0, :3, :3],					_lowerCamelCase,					atol=1E-4			)
	print('''Looks ok!'''			)

	if pytorch_dump_folder_path is not None:
		print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""			)
		model.save_pretrained(_lowerCamelCase			)
		print(F"""Saving processor to {pytorch_dump_folder_path}"""			)
		processor.save_pretrained(_lowerCamelCase			)

	if push_to_hub:
		print(F"""Pushing model and processor for {model_name} to hub"""			)
		model.push_to_hub(F"""openmmlab/{model_name}"""			)
		processor.push_to_hub(F"""openmmlab/{model_name}"""			)


if __name__ == "__main__":
					UpperCAmelCase        =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""upernet-swin-tiny""",
					    type=str,
					    choices=[f"""upernet-swin-{size}""" for size in ["""tiny""", """small""", """base""", """large"""]],
					    help="""Name of the Swin + UperNet model you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)
					parser.add_argument(
					    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
					)

					UpperCAmelCase        =				parser.parse_args()
					convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	str      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use PerceiverImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


